Nature-Inspired Algorithms and Applications (eBook, PDF)

Redaktion: Balamurugan, S.; Sharma, Seema; Duggal, Sonia; Goyal, Dinesh; Sharma, Sachin; Jain, Anupriya

• Format: PDF

Produktbeschreibung

NATURE-INSPIRED ALGORITHMS AND APPLICATIONS The book's unified approach of balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Inspired by the world around them, researchers are gathering information that can be developed for use in areas where certain practical applications of nature-inspired computation and machine learning can be applied. This book is designed to enhance the reader's understanding of this process by portraying certain practical applications of nature-inspired algorithms (NIAs) specifically designed to solve complex real-world problems in data analytics and pattern recognition by means of domain-specific solutions. Since various NIAs and their multidisciplinary applications in the mechanical engineering and electrical engineering sectors; and in machine learning, image processing, data mining, and wireless networks are dealt with in detail in this book, it can act as a handy reference guide. Among the subjects of the 12 chapters are: * A novel method based on TRIZ to map real-world problems to nature problems * Applications of cuckoo search algorithm for optimization problems * Performance analysis of nature-inspired algorithms in breast cancer diagnosis * Nature-inspired computation in data mining * Hybrid bat-genetic algorithm-based novel optimal wavelet filter for compression of image data * Efficiency of finding best solutions through ant colony optimization techniques * Applications of hybridized algorithms and novel algorithms in the field of machine learning. Audience: Researchers and graduate students in mechanical engineering, electrical engineering, machine learning, image processing, data mining, and wireless networks will find this book very useful.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 384
• Erscheinungstermin: 18. November 2021
• Englisch
• ISBN-13: 9781119681991
• Artikelnr.: 63043950

Autorenporträt

S. Balamurugan, PhD is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents. Anupriya Jain, PhD is an associate professor at the Manav Rachna International Institute of Research and Studies, Faridabad, Haryana. Sachin Sharma, PhD is an assistant professor in computer applications at the Manav Rachna International Institute of Research and Studies, Faridabad, India. He has published more than 30 research papers in different areas of technology and has been a part of two patents as well. Dinesh Goyal, PhD is the Director at the Poornima Institute of Engineering and Technology, Jaipur, India. His research interests are related to information & network security, image processing, data analytics, and cloud computing, and has published more than 60 research articles. Sonia Duggal, PhD is an associate professor at the Manav Rachna International Institute of Research and Studies, Faridabad, Haryana. Seema Sharma is an assistant professor at the Manav Rachna International Institute of Research and Studies, Faridabad, India.

Inhaltsangabe

Preface xv

1 Introduction to Nature-Inspired Computing 1
N.M. Saravana Kumar, K. Hariprasath, N. Kaviyavarshini and A. Kavinya

1.1 Introduction 1

1.2 Aspiration From Nature 2

1.3 Working of Nature 3

1.4 Nature-Inspired Computing 4

1.4.1 Autonomous Entity 5

1.5 General Stochastic Process of Nature-Inspired Computation 6

1.5.1 NIC Categorization 8

1.5.1.1 Bioinspired Algorithm 9

1.5.1.2 Swarm Intelligence 10

1.5.1.3 Physical Algorithms 11

1.5.1.4 Familiar NIC Algorithms 12

References 30

2 Applications of Hybridized Algorithms and Novel Algorithms in the Field of Machine Learning 33
P. Mary Jeyanthi and A. Mansurali

2.1 Introduction of Genetic Algorithm 33

2.1.1 Background of GA 35

2.1.2 Why Natural Selection Theory Compared With the Search Heuristic Algorithm? 35

2.1.3 Working Sequence of Genetic Algorithm 35

2.1.3.1 Population 35

2.1.3.2 Fitness Among the Individuals 36

2.1.3.3 Selection of Fitted Individuals 36

2.1.3.4 Crossover Point 37

2.1.3.5 Mutation 37

2.1.4 Application of Machine Learning in GA 38

2.1.4.1 Genetic Algorithm Role in Feature Selection for ML Problem 38

2.1.4.2 Traveling Salesman Problem 39

2.1.4.3 Blackjack--A Casino Game 40

2.1.4.4 Pong Against AI--Evolving Agents (Reinforcement Learning) Using GA 41

2.1.4.5 SNAKE AI--Game 41

2.1.4.6 Genetic Algorithm's Role in Neural Network 42

2.1.4.7 Solving a Battleship Board Game as an Optimization Problem Which Was Initially Released by Milton Bradley in 1967 43

2.1.4.8 Frozen Lake Problem From OpenAI Gym 43

2.1.4.9 N-Queen Problem 44

2.1.5 Application of Data Mining in GA 44

2.1.5.1 Association Rules Generation 44

2.1.5.2 Pattern Classification With Genetic Algorithm 45

2.1.5.3 Genetic Algorithms in Stock Market Data Mining Optimization 46

2.1.5.4 Market Basket Analysis 46

2.1.5.5 Job Scheduling 46

2.1.5.6 Classification Problem 47

2.1.5.7 Hybrid Decision Tree--Genetic Algorithm to Data Mining 47

2.1.5.8 Genetic Algorithm--Optimization of Data Mining in Education 47

2.1.6 Advantages of Genetic Algorithms 47

2.1.7 Genetic Algorithms Demerits in the Current Era 48

2.2 Introduction to Artificial Bear Optimization (ABO) 50

2.2.1 Bear's Nasal Cavity 52

2.2.2 Artificial Bear ABO Gist 54

2.2.3 Implementation Based on Requirement 58

2.2.3.1 Market Place 58

2.2.3.2 Industry-Specific 58

2.2.3.3 Semi-Structured or Unstructured Data 59

2.2.4 Merits of ABO 60

2.3 Performance Evaluation 61

2.4 What is Next? 62

References 63

3 Efficiency of Finding Best Solutions Through Ant Colony Optimization (ACO) Technique 67
K. Sasi Kala Rani and N. Pooranam

3.1 Introduction 68

3.1.1 Example of Optimization Process 69

3.1.2 Components of Optimization Algorithms 70

3.1.3 Optimization Techniques Based on Solutions 70

3.1.3.1 Optimization Techniques Based on Algorithms 72

3.1.4 Characteristics 73

3.1.5 Classes of Heuristic Algorithms 74

3.1.6 Metaheuristic Algorithms 75

3.1.6.1 Classification of Metaheuristic Algorithms: Nature-Inspired vs. Non-Nature-Inspired 75

3.1.6.2 Population-Based vs. Single-Point Search (Trajectory) 75

3.1.7 Data Processing Flow of ACO 76

3.2 A Case Study on Surgical Treatment in Operation Room